Successful Application of Special Log Interpretation Methodology for Estimation of Petrophysical Properties of Thin-Bedded Reservoirs of Vikulovskaya Layers of Krasnoleninskoe Oilfield

Martinov, M E (TNK-Nyagan) | Kozlov, A V (TNK-Nyagan) | Leskin, F Y (TNK-Nyagan) | Filimonov, A Y (Schlumberger) | Ezersky, D M (Schlumberger) | Egorov, S S (Schlumberger) | Blinov, V A (Schlumberger) | Weinheber, P.. (Schlumberger)

OnePetro 

Abstract The Vikulovskaya formation of Western Siberia is characterized by thinly-bedded, sand-shale layers. The vertical thickness of these layers ranges from a few millimeters to a few centimeters. This layered feature presents well known challenges for petrophysical analysis from standard logging suite data. These layers are typically beyond the vertical resolution of the standard tools so net-to-gross cannot be derived directly. The shale layers suppress the resistivity readings in the oil strata and the resulting low resistivity contrast makes it difficult to determine the oil-water contact. Finally, the ability to resolve the individual sand layers makes it impossible to accurately determine their water saturation. In this paper we discuss how these challenges were surmounted when performing a petrophysical evaluation of a dataset acquired in a recently drilled well in the Krasnoleninskoe field. This dataset consisted of full bore core and traditional ‘triple combo’ data. Additionally, we had NMR data, high resolution micro-imager data and formation tester pressure and fluid analysis data. By combining the measurements from the traditional tools with the resolution of the micro-imager data we were able estimate the desired petrophysical properties of the thinly-bedded layers individually. By using tools with different physics we were able to realize an independent quality control of the interpretation: stationary NMR measurements were used as porosity and irreducible water saturation reference, and formation tester data of direct inflow composition were used as a reference for fluid saturations. As a final check on our method we performed a digital integration of core and micro-imager data to validate our findings. The resultant workflow is concisely explained such that it can be easily applied to similar evaluation environments.

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